Despite substantial advances, single-image super-resolution (SISR) is al...
Recent papers have used machine learning architecture to fit low-order
f...
The prediction of molecular properties is a crucial task in the field of...
High Performance and Energy Efficiency are critical requirements for Int...
For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) ...
Recent advances in neural text-to-speech (TTS) models bring thousands of...
Sparse-view computed tomography (CT) – using a small number of projectio...
This paper proposes a technique for efficiently modeling dynamic humans ...
Near-infrared (NIR) image spectrum translation is a challenging problem ...
There has been an increasing use of master protocols in oncology clinica...
Hyperspectral images (HSI) have a large amount of spectral information
r...
Hyperspectral images (HSI) with abundant spectral information reflected
...
Spectral unmixing has been extensively studied with a variety of methods...
In clinical trials it is often desirable to test for superiority and
non...
When adopting a model-based formulation, solving inverse problems encoun...
In this paper, online game is studied, where at each time, a group of pl...
The top-down and bottom-up methods are two mainstreams of referring
segm...
Learning an effective global model on private and decentralized datasets...
Recent advances in robust semi-supervised learning (SSL) typically filte...
Graph convolutional networks (GCNs) are discriminative models that
direc...
In the early days of machine learning (ML), the emphasis was on developi...
Text-video retrieval is a challenging cross-modal task, which aims to al...
Heatmap-based anatomical landmark detection is still facing two unresolv...
Denoising diffusion probabilistic models (DDPMs) have shown promising
pe...
Training and inference with graph neural networks (GNNs) on massive grap...
Video frame interpolation(VFI) has witnessed great progress in recent ye...
Multiview clustering (MVC) aims to reveal the underlying structure of
mu...
Precise segmentation of residual tumor in breast cancer (PSRTBC) after
n...
Contrastive learning-based video-language representation learning approa...
Interactive segmentation enables users to segment as needed by providing...
Existing text-video retrieval solutions are, in essence, discriminant mo...
Vertical federated learning (VFL) is a distributed learning paradigm, wh...
Unsupervised spectral unmixing consists of representing each observed pi...
Unified visual grounding pursues a simple and generic technical route to...
Strategy card game is a well-known genre that is demanding on the intell...
Deep Reinforcement Learning combined with Fictitious Play shows impressi...
Object detection is a fundamental problem in computer vision, aiming at
...
In conventional dual-function radar-communication (DFRC) systems, the ra...
Gaussian processes (GPs) are an attractive class of machine learning mod...
Aerial-aquatic vehicles are capable to move in the two most dominant flu...
Graph neural networks (GNNs) are a class of effective deep learning mode...
The Position Embedding (PE) is critical for Vision Transformers (VTs) du...
Deconvolution is a widely used strategy to mitigate the blurring and noi...
Recent research showed that the dual-pixel sensor has made great progres...
Weakly supervised semantic segmentation is typically inspired by class
a...
Most video-and-language representation learning approaches employ contra...
Existing Graph Neural Networks (GNNs) follow the message-passing mechani...
As a powerful engine, vanilla convolution has promoted huge breakthrough...
Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance ...
Flow-guide synthesis provides a common framework for frame interpolation...